Field of the Invention
The present invention relates to information handling systems. More specifically, embodiments of the invention relate to using graph theory and network analytics and diagnostics for process optimization in manufacturing.
Description of the Related Art
As the value and use of information continues to increase, individuals and businesses seek additional ways to process and store information. One option available to users is information handling systems. An information handling system generally processes, compiles, stores, and/or communicates information or data for business, personal, or other purposes thereby allowing users to take advantage of the value of the information. Because technology and information handling needs and requirements vary between different users or applications, information handling systems may also vary regarding what information is handled, how the information is handled, how much information is processed, stored, or communicated, and how quickly and efficiently the information may be processed, stored, or communicated. The variations in information handling systems allow for information handling systems to be general or configured for a specific user or specific use such as financial transaction processing, airline reservations, enterprise data storage, global communications, manufacturing, or process control. In addition, information handling systems may include a variety of hardware and software components that may be configured to process, store, and communicate information and may include one or more computer systems, data storage systems, and networking systems.
It is known to analyze or control manufacturing operations using information handling systems. An issue in manufacturing operations is the difficulty of tracking manufacturing information such as the genealogy of parts or batches through consecutive processing steps. For example, a common application for root cause and failure analysis in batch manufacturing operations, which are often used when manufacturing pharmaceuticals, is to track the genealogy of batches through the process tree. Usually, such process trees include multiple processing steps where multiple input batches upstream can combine into single batches or containers downstream and single input batches upstream can split into multiple output batches or containers downstream. Process trees may also include branches that connect back to upstream batches or processing steps, e.g., to support rework.
When such processes include relatively large numbers of processing steps and individual input batches, parts, or suppliers (e.g., 40 processing steps and hundreds of batches and hundreds of suppliers, supplying material flowing through the processing steps), complex process trees emerge defining the degree to which each batch is related to other batches through common antecedents or ancestors upstream or off-springs downstream in the process tree. Tracking manufacturing information such as quality measurements, concentrations of chemicals and active ingredients, or to compute statistics centered at a particular process step (also called “unit operation”) for measurements or attributes observed upstream or downstream presents mostly a data management challenge.
Many process monitoring and root cause analysis around batch and other multi-step manufacturing processes typically rely on aggregation of measurements upstream and sometimes downstream at a particular process step (i.e., a unit operation), with the goal to perform meaningful statistical process control, comparisons between good and bad batches, etc.